genai-hub-foundation-models

Compare original and translation side by side

🇺🇸

Original

English

🇨🇳

Translation

Chinese

Rules

规则

Fetch the live model list when the model name is unknown or unconfirmed — skip if the user already named a specific model.
```
executable-id
```
and
```
model-name
```
values from this skill are the inputs needed by
```
aicore-lifecycle-management
```
to create a deployment.
If credentials are not configured, invoke
```
aicore-admin-resources
```
first.
Before executing any curl example, get a Bearer token with:
bash
```
export TOKEN=$(uv run skills/aicore-admin-resources/scripts/get_token.py)
```
Never add
```
2>&1
```
or pipe the command — that prints the raw JWT into the conversation.

当模型名称未知或未确认时，获取实时模型列表——如果用户已指定具体模型，则跳过此操作。
此 skill 中的
```
executable-id
```
和
```
model-name
```
值是
```
aicore-lifecycle-management
```
创建部署所需的输入。
如果未配置凭据，请先调用
```
aicore-admin-resources
```
。
执行任何 curl 示例之前，使用以下命令获取 Bearer token：
bash
```
export TOKEN=$(uv run skills/aicore-admin-resources/scripts/get_token.py)
```
切勿添加
```
2>&1
```
或对命令使用管道——否则会将原始 JWT 输出到对话中。

List Available Models

可用模型列表

bash

undefined

bash

undefined

Show all options and examples:

uv run scripts/list_foundation_models.py --help

One provider only (e.g. Azure OpenAI):

uv run scripts/list_foundation_models.py --executable-id azure-openai

Detailed view (versions, capabilities, context length, streaming support):

uv run scripts/list_foundation_models.py --response-format detailed

Machine-readable JSON for scripting:

uv run scripts/list_foundation_models.py --json | jq '.[].model' uv run scripts/list_foundation_models.py --response-format detailed --json | jq '.[] | select(.model == "gpt-4o") | .versions'

Models supported by the Orchestration service only:

uv run scripts/list_foundation_models.py --scenario-id orchestration

---

uv run scripts/list_foundation_models.py --scenario-id orchestration

---

Provider → Executable ID Mapping

提供商 → Executable ID 映射

Provider	`--executable-id`
Azure OpenAI	`azure-openai`
Anthropic	`aws-bedrock`
Google	`gcp-vertexai`
Amazon Bedrock	`aws-bedrock`
Mistral AI	`aicore-mistralai`
Cohere	`aicore-cohere`
Perplexity	`perplexity-ai`
NVIDIA	`aicore-nvidia`
Open Source	`aicore-opensource`
SAP	`aicore-sap`

Supported models in your instance may differ from this table — use the script to get the live list.

提供商	`--executable-id`
Azure OpenAI	`azure-openai`
Anthropic	`aws-bedrock`
Google	`gcp-vertexai`
Amazon Bedrock	`aws-bedrock`
Mistral AI	`aicore-mistralai`
Cohere	`aicore-cohere`
Perplexity	`perplexity-ai`
NVIDIA	`aicore-nvidia`
Open Source	`aicore-opensource`
SAP	`aicore-sap`

你的实例中支持的模型可能与此表不同——请使用脚本获取实时列表。

Create a Deployment

创建部署

Once you have the

executable-id

and

model-name

, check whether a RUNNING deployment for that model already exists:

bash

uv run skills/aicore-lifecycle-management/scripts/get_deployments.py \
  --scenario foundation-models --status RUNNING --json

If one is found, use

AskUserQuestion

to ask whether to reuse it or create a new one. Then invoke

aicore-lifecycle-management

with both values.

The

modelName

value must exactly match the catalog output — a mismatch causes an "Invalid Configuration" error at deployment time, not at configuration creation.

获得

executable-id

和

model-name

后，检查是否已存在该模型的 RUNNING 部署：

bash

uv run skills/aicore-lifecycle-management/scripts/get_deployments.py \
  --scenario foundation-models --status RUNNING --json

如果找到现有部署，请使用

AskUserQuestion

询问用户是复用该部署还是创建新部署。然后使用这两个值调用

aicore-lifecycle-management

。

modelName

值必须与目录输出完全匹配——不匹配会在部署时引发“Invalid Configuration”错误，而不会在创建设置时出现。

When to Load Reference Files

何时加载参考文件

Trigger	Load
Curl inference examples, provider-specific endpoint paths, or about to call a deployed model	`references/MODELS.md`

触发条件	加载
Curl 推理示例、特定提供商的 endpoint 路径，或即将调用已部署模型	`references/MODELS.md`

Finding the Deployment URL for a Curl Call

查找用于 Curl 调用的部署 URL

Get the
executable-id
— look it up in the Provider → Executable ID table above.

Find the running deployment:

bash

uv run skills/aicore-lifecycle-management/scripts/get_deployments.py \
  --scenario foundation-models --executable-id <executable-id> --status RUNNING --json
# use .deployment_url and .resource_group from the matching entry

See
```
references/MODELS.md
```
for export setup and curl examples per provider.

获取
executable-id
——通过上方的提供商 → Executable ID 映射表查找。

查找正在运行的部署：

bash

uv run skills/aicore-lifecycle-management/scripts/get_deployments.py \
  --scenario foundation-models --executable-id <executable-id> --status RUNNING --json
# use .deployment_url and .resource_group from the matching entry

查看
```
references/MODELS.md
```
，了解导出环境变量的配置方式以及各提供商的 curl 示例。

Handoffs

任务移交

Credential/auth error? → invoke
```
aicore-admin-resources
```
Ready to create a deployment with the model you found? → invoke
```
aicore-lifecycle-management
```

遇到凭据或身份验证错误？ → 调用
```
aicore-admin-resources
```
已准备好使用找到的模型创建部署？ → 调用
```
aicore-lifecycle-management
```